This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work.

'Scale the model, scale the data, scale the compute' is the reigning sentiment in the world of generative AI today.

Audio editing is applicable for various purposes, such as adding background sound effects, replacing a musical instrument, and repairing damaged audio. Recently, some diffusion-based methods achieved zero-shot audio editing by using a diffusion and denoising process conditioned on the text description of the output audio. However, these methods still have some problems: 1) they have not been trained on editing tasks and cannot ensure good editing effects; 2) they can erroneously modify audio segments that do not require editing; 3) they need a complete description of the output audio, which is not always available or necessary in practical scenarios. In this work, we propose AUDIT, an instruction-guided audio editing model based on latent diffusion models. Specifically, \textbf{AUDIT} has three main design features: 1) we construct triplet training data (instruction, input audio, output audio) for different audio editing tasks and train a diffusion model using instruction and input (to be edited) audio as conditions and generating output (edited) audio; 2) it can automatically learn to only modify segments that need to be edited by comparing the difference between the input and output audio; 3) it only needs edit instructions instead of full target audio descriptions as text input. AUDIT achieves state-of-the-art results in both objective and subjective metrics for several audio editing tasks (e.g., adding, dropping, replacement, inpainting, super-resolution). Demo samples are available at https://audit-demopage.github.io/.

We study fixed-confidence best-arm identification (BAI) where a cheap but potentially biased proxy (e.g., LLM judge) is available for every sample, while an expensive ground-truth label can only be acquired selectively when using a human for auditing. Unlike classical multi-fidelity BAI, the proxy is biased (arm- and context-dependent) and ground truth is selectively observed. Consequently, standard multi-fidelity methods can mis-select the best arm, and uniform auditing, though accurate, wastes scarce resources and is inefficient. We prove that without bias correction and propensity adjustment, mis-selection probability may not vanish (even with unlimited proxy data). We then develop an estimator for the mean of each arm that combines proxy scores with inverse-propensity-weighted residuals and form anytime-valid confidence sequences for that estimator. Based on the estimator and confidence sequence, we propose an algorithm that adaptively selects and audits arms. The algorithm concentrates audits on unreliable contexts and close arms and we prove that a plug-in Neyman rule achieves near-oracle audit efficiency. Numerical experiments confirm the theoretical guarantees and demonstrate the superior empirical performance of the proposed algorithm.

Indonesia's government has filed multiple lawsuits seeking more than $200m in damages against six firms, after deadly floods wreaked havoc across Sumatra, killing more than 1,000 people last year, although environmentalists criticised the moves as inadequate. Environmentalists, experts and the government pointed the finger at deforestation for its role in last year's disaster that washed torrents of mud and wooden logs into villages across the northwestern part of the island. The sum represents both fines for damage and the proposed monetary value of recovery efforts. The suits were filed to courts on Thursday in Jakarta and North Sumatra's Medan, the ministry added. "We firmly uphold the principle of polluter pays," Environment Minister Hanif Faisol Nurofiq said in a statement.

General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training corpora. Our audit of 14,000 web domains provides an expansive view of crawlable web data and how codified data use preferences are changing over time. We observe a proliferation of AI-specific clauses to limit use, acute differences in restrictions on AI developers, as well as general inconsistencies between websites' expressed intentions in their Terms of Service and their robots.txt. We diagnose these as symptoms of ineffective web protocols, not designed to cope with the widespread re-purposing of the internet for AI.

Ben & Jerry's brand could be destroyed, says co-founder Ben & Jerry's will be destroyed as a brand if it remains with parent company Magnum, the company's co-founder Ben Cohen has told the BBC. His remarks are the latest in a long-running spat between the ice cream brand and its parent company over its ability to express its social activism and the continued independence of its board. The comments came on the day that the Magnum Ice Cream Company (TMICC) started trading on the European stock market - spinning off from owner Unilever. A spokesperson for Magnum said the firm wanted to build and strengthen Ben & Jerry's powerful, non-partisan values-based position in the world. Ben & Jerry's was sold to Unilever in 2000 in a deal which allowed it to retain an independent board and the right to make decisions about its social mission.